Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models?

There have been many efforts to try to understand what gram-matical knowledge (e.g., ability to understand the part of speech of a token) is encoded in large pre-trained language models (LM). This is done through 'Edge Probing' (EP) tests: simple ML models that predict the grammatical properties ofa span (whether it has a particular part of speech) using only the LM's token representations. However, most NLP applications use fine-tuned LMs. Here, we ask: if a LM is fine-tuned, does the encoding of linguistic information in it change, as measured by EP tests? Conducting experiments on multiple question-answering (QA) datasets, we answer that question negatively: the EP test results do not change significantly when the fine-tuned QA model performs well or in adversarial situations where the model is forced to learn wrong correlations. However, a critical analysis of the EP task datasets reveals that EP models may rely on spurious correlations to make predictions. This indicates even if fine-tuning changes the encoding of such knowledge, the EP tests might fail to measure it.